library(dplyr)
library(Seurat)
library(scater)
library(tibble)

sce <- readRDS('sce_jul_3.rds')
# seu <- CreateSeuratObject(counts=counts(sce), project = "EA3")
# seu <- NormalizeData(seu)
# seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
# plot1 <- VariableFeaturePlot(seu)
# plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
# CombinePlots(plots = list(plot1, plot2))
seu <- readRDS("seu_jun_21")
top10 <- head(VariableFeatures(seu), 10)
# all.genes <- rownames(seu)
# seu <- ScaleData(seu, features = all.genes)
# seu <- RunPCA(seu, features = VariableFeatures(object = seu))
VizDimLoadings(seu, dims = 1:2, reduction = "pca")

PCA

DimPlot(seu, reduction = "pca")

# seu <- JackStraw(seu, num.replicate = 100)
# seu <- ScoreJackStraw(seu, dims = 1:20)
JackStrawPlot(seu, dims = 1:20)
#> Warning: Removed 28473 rows containing missing values (geom_point).

ElbowPlot(seu)

head(Idents(seu), 5)
#> A01_EA1 A02_EA1 A03_EA1 A04_EA1 A05_EA1 
#>       1       3       3       0       3 
#> Levels: 0 1 2 3 4

UMAP

my_df <- data.frame(colData(sce)) %>% dplyr::mutate( somite_pairs = case_when(somitepairs == "19-20" ~ "20-22",
                                         somitepairs == "20" ~ "20-22",
                                         somitepairs == "20-21" ~ "20-22",
                                         somitepairs == "21" ~ "20-22",
                                         somitepairs == "21-22" ~ "20-22",
                                         somitepairs == "22" ~ "20-22",
                                         somitepairs == "22-23" ~ "23-25",
                                         somitepairs == "23" ~ "23-25",
                                         somitepairs == "23-24" ~ "23-25",
                                         somitepairs == "24" ~ "23-25",
                                         somitepairs == "24-25" ~ "23-25",
                                         somitepairs == "25" ~ "23-25",
                                         somitepairs == "25-26" ~ "26-27",
                                         somitepairs == "26" ~ "26-27",
                                          somitepairs == "27-28" ~ "26-27"))

seu@meta.data <- cbind(seu@meta.data,my_df[,c("genotype","phenotype", "somite_pairs")])
DimPlot(seu, reduction = "umap",cols = c("#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"))

UMAP Geno Pheno

p1 <- DimPlot(seu, reduction = "umap", group.by = "genotype")
p2 <- DimPlot(seu, reduction = "umap", group.by = "phenotype")
p3 <- left_join(rownames_to_column(p1$data,"sample_id"),rownames_to_column(p2$data,"sample_id"), by = "sample_id") %>% dplyr::select(sample_id,UMAP_1.x,UMAP_2.x,genotype,phenotype) %>% dplyr::rename( UMAP_1 = UMAP_1.x , UMAP_2 = UMAP_2.x)

ggplot(p3,aes(UMAP_1,UMAP_2,colour = phenotype, shape = genotype, alpha=0.5)) + geom_point(size = 3) + theme_classic() + scale_colour_manual(values = c("#009E73","#F0E442", "#D55E00","#0072B2" )) + guides(alpha = FALSE)

UMAP

somite pairs

p1 <- DimPlot(seu, reduction = "umap", group.by = "genotype")
p2 <- DimPlot(seu, reduction = "umap", group.by = "somite_pairs")
p3 <- left_join(rownames_to_column(p1$data,"sample_id"),rownames_to_column(p2$data,"sample_id"), by = "sample_id") %>% dplyr::select(sample_id,UMAP_1.x,UMAP_2.x,genotype,somite_pairs) %>% dplyr::rename( UMAP_1 = UMAP_1.x , UMAP_2 = UMAP_2.x)

ggplot(p3,aes(UMAP_1,UMAP_2,colour = somite_pairs, shape = genotype, alpha=0.5)) + geom_point(size = 3) + theme_classic() + scale_colour_manual(values = c("#009E73","#F0E442", "#D55E00","#0072B2" )) + guides(alpha = FALSE)

Cluster 0 markers

cluster0.markers <- FindMarkers(seu, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )

Cluster 1 markers

cluster0.markers <- FindMarkers(seu, ident.1 = 1, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )

Cluster 2 markers

cluster0.markers <- FindMarkers(seu, ident.1 = 2, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )

Cluster 3 markers

cluster0.markers <- FindMarkers(seu, ident.1 = 3, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )

Cluster 4 markers

cluster0.markers <- FindMarkers(seu, ident.1 = 4, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )

Top 2 from each cluster

FeaturePlot(seu, features = c("Adm", "Pde4b", "Gja4", "Gja5", "Gm13461", "Hspd1-ps3", "Gm5518", "Sumo2", 
    "Gpc3", "Nnat"))

Differential gene expression

Cluster 0 v 1

library(MAST)
library(DT)

cluster_0_prohsc_v_cluster_1_pro_hsc <- FindMarkers(seu, ident.1 = 0, ident.2 = 1 , test.use = "MAST")

datatable(cluster_0_prohsc_v_cluster_1_pro_hsc,  extensions = 'Buttons', options = list (dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')))

ProHsc Cluster 0 v 1

library(MAST)
library(DT)
cluster_0_pro_hsc <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 0 & phenotype == "Pro-HSC")
cell_id_cluster_0_pro_hsc <- cluster_0_pro_hsc$ID
cluster_1_pro_hsc <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 1 & phenotype == "Pro-HSC") 
cell_id_cluster_1_pro_hsc <- cluster_1_pro_hsc$ID

cluster_0_prohsc_v_cluster_1_pro_hsc <- FindMarkers(seu, ident.1 = cell_id_cluster_0_pro_hsc, ident.2 = cell_id_cluster_1_pro_hsc , test.use = "MAST")

datatable(cluster_0_prohsc_v_cluster_1_pro_hsc, extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy','csv','excel')))

Hemogenic endothelium Cluster 0 v 1

cluster_0_HE <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 0 & phenotype == "Hemogenic Endothelium")
cell_id_cluster_0_HE <- cluster_0_HE$ID
cluster_1_HE <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 1 & phenotype == "Hemogenic Endothelium") 
cell_id_cluster_1_HE <- cluster_1_HE$ID

cluster_0_HE_v_cluster_1_HE <- FindMarkers(seu, ident.1 = cell_id_cluster_0_HE, ident.2 = cell_id_cluster_1_HE , test.use = "MAST")

datatable(cluster_0_HE_v_cluster_1_HE, extensions = 'Buttons', options = list (dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')))
---
title: "Azzoni data VE-Cad+23GFP+ reclassified"
author: "Chela James"
date: "`r Sys.Date()`"
output:
  html_document:
    code_folding: hide
    code_download: true
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  message = FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.width=12,
  fig.height=8,
  fig.align = "center"
)
```


```{r}
library(dplyr)
library(Seurat)
library(scater)
library(tibble)

sce <- readRDS('sce_jul_3.rds')
# seu <- CreateSeuratObject(counts=counts(sce), project = "EA3")
# seu <- NormalizeData(seu)
# seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
# plot1 <- VariableFeaturePlot(seu)
# plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
# CombinePlots(plots = list(plot1, plot2))
seu <- readRDS("seu_jun_21")
top10 <- head(VariableFeatures(seu), 10)
# all.genes <- rownames(seu)
# seu <- ScaleData(seu, features = all.genes)
# seu <- RunPCA(seu, features = VariableFeatures(object = seu))
VizDimLoadings(seu, dims = 1:2, reduction = "pca")
```

# PCA

```{r}
DimPlot(seu, reduction = "pca")
```

```{r}
# seu <- JackStraw(seu, num.replicate = 100)
# seu <- ScoreJackStraw(seu, dims = 1:20)
JackStrawPlot(seu, dims = 1:20)
```

```{r}
ElbowPlot(seu)
```

```{r}
head(Idents(seu), 5)
```

# UMAP

```{r}
my_df <- data.frame(colData(sce)) %>% dplyr::mutate( somite_pairs = case_when(somitepairs == "19-20" ~ "20-22",
                                         somitepairs == "20" ~ "20-22",
                                         somitepairs == "20-21" ~ "20-22",
                                         somitepairs == "21" ~ "20-22",
                                         somitepairs == "21-22" ~ "20-22",
                                         somitepairs == "22" ~ "20-22",
                                         somitepairs == "22-23" ~ "23-25",
                                         somitepairs == "23" ~ "23-25",
                                         somitepairs == "23-24" ~ "23-25",
                                         somitepairs == "24" ~ "23-25",
                                         somitepairs == "24-25" ~ "23-25",
                                         somitepairs == "25" ~ "23-25",
                                         somitepairs == "25-26" ~ "26-27",
                                         somitepairs == "26" ~ "26-27",
                                          somitepairs == "27-28" ~ "26-27"))

seu@meta.data <- cbind(seu@meta.data,my_df[,c("genotype","phenotype", "somite_pairs")])
DimPlot(seu, reduction = "umap",cols = c("#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7"))
```

# UMAP Geno Pheno

```{r}
p1 <- DimPlot(seu, reduction = "umap", group.by = "genotype")
p2 <- DimPlot(seu, reduction = "umap", group.by = "phenotype")
p3 <- left_join(rownames_to_column(p1$data,"sample_id"),rownames_to_column(p2$data,"sample_id"), by = "sample_id") %>% dplyr::select(sample_id,UMAP_1.x,UMAP_2.x,genotype,phenotype) %>% dplyr::rename( UMAP_1 = UMAP_1.x , UMAP_2 = UMAP_2.x)

ggplot(p3,aes(UMAP_1,UMAP_2,colour = phenotype, shape = genotype, alpha=0.5)) + geom_point(size = 3) + theme_classic() + scale_colour_manual(values = c("#009E73","#F0E442", "#D55E00","#0072B2" )) + guides(alpha = FALSE)

```

# UMAP
## somite pairs

```{r}
p1 <- DimPlot(seu, reduction = "umap", group.by = "genotype")
p2 <- DimPlot(seu, reduction = "umap", group.by = "somite_pairs")
p3 <- left_join(rownames_to_column(p1$data,"sample_id"),rownames_to_column(p2$data,"sample_id"), by = "sample_id") %>% dplyr::select(sample_id,UMAP_1.x,UMAP_2.x,genotype,somite_pairs) %>% dplyr::rename( UMAP_1 = UMAP_1.x , UMAP_2 = UMAP_2.x)

ggplot(p3,aes(UMAP_1,UMAP_2,colour = somite_pairs, shape = genotype, alpha=0.5)) + geom_point(size = 3) + theme_classic() + scale_colour_manual(values = c("#009E73","#F0E442", "#D55E00","#0072B2" )) + guides(alpha = FALSE)

```


# Cluster 0 markers

```{r}
cluster0.markers <- FindMarkers(seu, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )
```

# Cluster 1 markers

```{r}
cluster0.markers <- FindMarkers(seu, ident.1 = 1, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )
```

# Cluster 2 markers

```{r}
cluster0.markers <- FindMarkers(seu, ident.1 = 2, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )
```

# Cluster 3 markers

```{r}
cluster0.markers <- FindMarkers(seu, ident.1 = 3, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )
```

# Cluster 4 markers

```{r}
cluster0.markers <- FindMarkers(seu, ident.1 = 4, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
VlnPlot(seu, features = rownames(cluster0.markers)[1:6] )
```

# Top 2 from each cluster

```{r}
FeaturePlot(seu, features = c("Adm", "Pde4b", "Gja4", "Gja5", "Gm13461", "Hspd1-ps3", "Gm5518", "Sumo2", 
    "Gpc3", "Nnat"))
```

# Differential gene expression
## Cluster 0 v 1

```{r}
library(MAST)
library(DT)

cluster_0_prohsc_v_cluster_1_pro_hsc <- FindMarkers(seu, ident.1 = 0, ident.2 = 1 , test.use = "MAST")

datatable(cluster_0_prohsc_v_cluster_1_pro_hsc,  extensions = 'Buttons', options = list (dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')))

```

## ProHsc Cluster 0 v 1

```{r}
library(MAST)
library(DT)
cluster_0_pro_hsc <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 0 & phenotype == "Pro-HSC")
cell_id_cluster_0_pro_hsc <- cluster_0_pro_hsc$ID
cluster_1_pro_hsc <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 1 & phenotype == "Pro-HSC") 
cell_id_cluster_1_pro_hsc <- cluster_1_pro_hsc$ID

cluster_0_prohsc_v_cluster_1_pro_hsc <- FindMarkers(seu, ident.1 = cell_id_cluster_0_pro_hsc, ident.2 = cell_id_cluster_1_pro_hsc , test.use = "MAST")

datatable(cluster_0_prohsc_v_cluster_1_pro_hsc, extensions = 'Buttons', options = list(dom = 'Bfrtip', buttons = c('copy','csv','excel')))

```

## Hemogenic endothelium Cluster 0 v 1

```{r}
cluster_0_HE <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 0 & phenotype == "Hemogenic Endothelium")
cell_id_cluster_0_HE <- cluster_0_HE$ID
cluster_1_HE <- seu@meta.data %>% rownames_to_column("ID") %>% dplyr::filter(seurat_clusters == 1 & phenotype == "Hemogenic Endothelium") 
cell_id_cluster_1_HE <- cluster_1_HE$ID

cluster_0_HE_v_cluster_1_HE <- FindMarkers(seu, ident.1 = cell_id_cluster_0_HE, ident.2 = cell_id_cluster_1_HE , test.use = "MAST")

datatable(cluster_0_HE_v_cluster_1_HE, extensions = 'Buttons', options = list (dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')))
```


